{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A study on a square dielectric waveguide using MEEP\n",
    "\n",
    "In these notes, we calculate the electric field, Green's function and modified spontaneous emission rate of an atom in presence of a square dielectric waveguide running [MEEP](http://ab-initio.mit.edu/wiki/index.php/Meep). \n",
    "\n",
    "This is an [IJulia notebook](https://github.com/JuliaLang/IJulia.jl), which provides a nice\n",
    "browser-based [Jupyter](http://jupyter.org/) interface to the [Julia language](http://julialang.org/), a high-level dynamic language (similar to Matlab or Python+SciPy) for technical computing.  The notebook allows us to combine code and results in one place.\n",
    "\n",
    "\n",
    "## 3d dielectric waveguide\n",
    "\n",
    "Our simulation is based on the file `sqwg.ctl` in the same folder, which computes the e-field components of a square waveguide over time. The waveguide has a square cross-section of a width=0.3 ($\\mu m$ is the unit of length) and a index of refraction of $n_{core}=1.98$ in vacuum. We use the [unit convention of MEEP](http://ab-initio.mit.edu/wiki/index.php/Meep_Introduction#Units_in_Meep) throughout this notebook, that is to treat the speed of light in vacuum $c=1$ and use a unit length $a=1\\mu m$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let me assume you are running on a machine with `meep-mpi` installed.  It is easy to install on Ubuntu Linux with `apt-get install meep meep-mpi h5utils`.  There is also a Python interface of MEEP which can be installed using [Filip Dominec's scropt](https://github.com/FilipDominec/python-meep-install) on a Linux/Unix machine. \n",
    "\n",
    "To plot out the results, it requires `HDF5` and `PyPlot` in Julia. You can add these packages by running `Pkg.add(\"HDF5\")`, for example, in Julia or in this notebook. \n",
    "\n",
    "At the shell, we would normally run `meep-mpi \"sqwg.ctl\" > sqwg.out`, but we can also do this directly from Julia with the `run` and `pipeline` functions. As I am using a multi-core machine, I will also use MPI to accelerate the computation by using `mpirun -n` where `-n` switch specifies the number of cores in using for the computing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n",
      "\n",
      "Some deprecated features have been used.  Set the environment\n",
      "variable GUILE_WARN_DEPRECATED to \"detailed\" and rerun the\n",
      "program to get more information.  Set it to \"no\" to suppress\n",
      "this message.\n"
     ]
    }
   ],
   "source": [
    "run(pipeline(`mpirun -n 54 meep-mpi \"sqwg.ctl\"`, \"sqwg.out\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sqwg.out` file is now a dump of all the output from the `meep-mpi` run, basically a log of the computational process. Intermingled in this output are a sequence of lines beginning with prefixes like `creating output file ` which contain quatation-mark-delimited text of the data output file names.\n",
    "\n",
    "To pull out this data file names at a Unix command-line, the easiest thing is to use the `grep` command (which extracts lines matching a certain pattern) combined with the `cut` command (to strip out the quatation marks or delimiters, leaving only the content inside of the pairs of quatation marks).  We also need to cut the `./` string from each line. We can extract these to a file `datafilenames.dat` at the command line by `grep \"creating output file \" sqwg.out | cut -d '\"' -f2 | cut -d'/' -f2- > datafilenames.dat`, or in Julia by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqwg-Ert.h5\n",
      "sqwg-eps-000000000.h5\n",
      "sqwg-ex-000010000.h5\n",
      "sqwg-ey-000010000.h5\n",
      "sqwg-ez-000010000.h5\n",
      "sqwg-ex-000020000.h5\n",
      "sqwg-ey-000020000.h5\n",
      "sqwg-ez-000020000.h5\n",
      "sqwg-ex-000030000.h5\n",
      "sqwg-ey-000030000.h5\n",
      "sqwg-ez-000030000.h5\n",
      "sqwg-ex-000040000.h5\n",
      "sqwg-ey-000040000.h5\n",
      "sqwg-ez-000040000.h5\n",
      "sqwg-ex-000050000.h5\n",
      "sqwg-ey-000050000.h5\n",
      "sqwg-ez-000050000.h5\n",
      "sqwg-ex-000060000.h5\n",
      "sqwg-ey-000060000.h5\n",
      "sqwg-ez-000060000.h5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "run(pipeline(`grep \"creating output file \" sqwg.out`, `cut -d '\"' -f2`, `cut -d'/' -f2-`, \"datafilenames.dat\"))\n",
    "\n",
    "# display the contents of the resultying `datafilenames.dat` file:\n",
    "println(readstring(\"datafilenames.dat\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can read in this data as a matrix of numbers by the `readcsv` function in Julia (csv = [comma-separated values](https://en.wikipedia.org/wiki/Comma-separated_values)), where the `header=true` option means that it reads the first line as list of strings describing each column, followed by data on subsequent lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20×1 Array{Any,2}:\n",
       " \"sqwg-Ert.h5\"          \n",
       " \"sqwg-eps-000000000.h5\"\n",
       " \"sqwg-ex-000010000.h5\" \n",
       " \"sqwg-ey-000010000.h5\" \n",
       " \"sqwg-ez-000010000.h5\" \n",
       " \"sqwg-ex-000020000.h5\" \n",
       " \"sqwg-ey-000020000.h5\" \n",
       " \"sqwg-ez-000020000.h5\" \n",
       " \"sqwg-ex-000030000.h5\" \n",
       " \"sqwg-ey-000030000.h5\" \n",
       " \"sqwg-ez-000030000.h5\" \n",
       " \"sqwg-ex-000040000.h5\" \n",
       " \"sqwg-ey-000040000.h5\" \n",
       " \"sqwg-ez-000040000.h5\" \n",
       " \"sqwg-ex-000050000.h5\" \n",
       " \"sqwg-ey-000050000.h5\" \n",
       " \"sqwg-ez-000050000.h5\" \n",
       " \"sqwg-ex-000060000.h5\" \n",
       " \"sqwg-ey-000060000.h5\" \n",
       " \"sqwg-ez-000060000.h5\" "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafilenames = readcsv(\"datafilenames.dat\", header=false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first file is the meshgrid data of $\\epsilon$ or the electric permitivity of the waveguide in space. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot the dispersion relation.  I'll use the [PyPlot](https://github.com/stevengj/PyPlot.jl) package in Julia, which is an interface to the sophisticated [matplotlib](http://matplotlib.org/) Python plotting library.   We'll plot three things:\n",
    "\n",
    "* The waveguide structure in terms of $\\epsilon$.\n",
    "* Plot the light field components.\n",
    "* Calculate the waveguide-modified spontaneous decay rate for the dipole.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll do the same thing for the cases that the dipole is orientated in other directions and placed in different locations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the index of refraction profile of the waveguide\n",
    "\n",
    "MEEP needs to mesh the grid of the simulation cell to perform the computation. It is good to plot out the mesh of index of refraction in space and find out how good is the mesh resolution. This can be done by plotting out the output eps file in a standard [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) format, which ends in `.h5`.  A single HDF5 file can contain multiple \"datasets\", e.g. multiple field components or real+imaginary parts.\n",
    "\n",
    "In `sqwg.ctl`, we request some eps (or square of the index of refraction) output by a line `((at-beginning output-epsilon))` at the end of the file, which performs a simple meshing computation at the beginning and outputs $\\varepsilon(x,y,z)$.\n",
    "It is a good idea to check this file (plot $\\varepsilon$) to make sure the structure is what you think it is.  We can use the `h5ls` command-line tool to see what is inside this file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eps                      Dataset {63, 63, 70}\n"
     ]
    }
   ],
   "source": [
    "run(`h5ls sqwg-eps-000000.00.h5`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MEEP in general supports a $3\\times3$ anisotropic $\\varepsilon$ tensor, and the file contains all of the components of this (symmetric) tensor and its inverse.   However, for the most part we are just interested in `eps`, which is a \"scalarized\" $\\varepsilon$ formed from the average eigenvalue of $\\varepsilon$.\n",
    "\n",
    "Any serious technical language or plotting package these days supports HDF5.  In Julia's case, we can read HDF5 through the [HDF5](https://github.com/JuliaLang/HDF5.jl) package, using the `h5read(filename, dataset)` function. It actually reads the dataset of eps in a reversed order of $(x,y,z)$ dimensions so that we need to permute the dimensions backwards before plotting: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[34mINFO: Recompiling stale cache file /home/qxd/.julia/lib/v0.5/HDF5.ji for module HDF5.\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqwg-eps-000000000.h5("
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f0f9a7ea650>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "490,490,360)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.text.Text object at 0x7f0f9798f450>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using HDF5\n",
    "filename=datafilenames[2]\n",
    "print(filename)\n",
    "ɛ = h5read(join(filename), \"eps\") #\"sqwg-eps-000000.00.h5\",\"eps\")\n",
    "ɛ = permutedims(ɛ,[3,2,1])\n",
    "print(size(ɛ))\n",
    "lenx = length(ɛ[:,63,70])\n",
    "leny = length(ɛ[63,:,70])\n",
    "lenz = length(ɛ[63,63,:])\n",
    "#println(convert(Int64,floor(lenz/2)))\n",
    "x = linspace(-3.15,3.15, lenx)\n",
    "y = linspace(-3.15,3.15, leny)\n",
    "z = linspace(-3.5,3.5, lenz)\n",
    "xygrid = repmat(x',leny,1)\n",
    "yxgrid = repmat(y,1,lenx)\n",
    "zygrid = repmat(z',leny,1)\n",
    "yzgrid = repmat(y,1,lenz)\n",
    "\n",
    "fig = figure(\"index of refraction plot\",figsize=(10,10))\n",
    "ax = fig[:add_subplot](1,2,1)\n",
    "cp = ax[:contour](xygrid, yxgrid, sqrt(ɛ[:,:,ceil(Int64,lenz/2)]), colors=\"black\", linewidth=2.0)\n",
    "ax[:clabel](cp, inline=1, fontsize=5)\n",
    "#ax = fig[:add_subplot](1,2,1,projection=\"3d\")\n",
    "#cp = ax[:plot_surface](xygrid, yxgrid, sqrt(ɛ[:,:,70]),rstride=2,edgecolors=\"k\", cstride=2, cmap=ColorMap(\"gray\"), alpha=0.8, linewidth=0.2)\n",
    "#contourf(X,Y, ɛ[:,:,70])\n",
    "xlabel(L\"x/\\mu m\")\n",
    "ylabel(L\"y/\\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.3,0.3)\n",
    "ylim(-0.3,0.3)\n",
    "tight_layout()\n",
    "gcf() # Needed for IJulia to plot inline\n",
    "\n",
    "subplot(1,2,2)\n",
    "ax = fig[:add_subplot](1,2,2)\n",
    "#cp = ax[:contour](zygrid, yzgrid, squeeze(sqrt(ɛ[63,:,:]),1), colors=\"black\", linewidth=1.0)\n",
    "#ax[:clabel](cp, inline=1, fontsize=1)\n",
    "#ax = fig[:add_subplot](1,2,2,projection=\"3d\")\n",
    "#cp = ax[:plot_surface](zygrid, yzgrid, squeeze(sqrt(ɛ[63,:,:]),1),rstride=2,edgecolors=\"k\", cstride=2, cmap=ColorMap(\"gray\"), alpha=0.8, linewidth=0.2)\n",
    "pcolor(zygrid, yzgrid, sqrt(ɛ[ceil(Int64,lenx/2),:,:]), cmap=\"cool\")\n",
    "colorbar(;orientation=\"vertical\",fraction=0.04)\n",
    "xlabel(L\"z/ \\mu m\")\n",
    "ylabel(L\"y/ \\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.3,0.3)\n",
    "ylim(-0.3,0.3)\n",
    "tight_layout()\n",
    "#gcf() # Needed for IJulia to plot inline\n",
    "title(L\"n(y,z)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, $\\sqrt{\\varepsilon}=n$ is 2 in the middle and 1 elsewhere with a gradual changes around the edges.  Now let's read the fields.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the field intensity at different times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, `sqwg-ex-000010.00.h5` contains the electric field (`ex`) at the 10th unit time, the unit time is defined by $T=\\frac{a}{c}$ with $a=1\\mu m$ being the unit length and $c=1$ being the speed of light in vacuum. It contains only one dataset, `ex`, which is the real part of the $E_x$ field. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ex.i                     Dataset {63, 63, 70}\n",
      "ex.r                     Dataset {63, 63, 70}\n"
     ]
    }
   ],
   "source": [
    "run(`h5ls sqwg-ex-000040.00.h5`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(490,360)\n",
      "(490,360)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HDF5-DIAG: Error detected in HDF5 (1.8.16) thread 139714466110464:\n",
      "  #000: ../../../src/H5F.c line 439 in H5Fis_hdf5(): unable open file\n",
      "    major: File accessibilty\n",
      "    minor: Not an HDF5 file\n",
      "  #001: ../../../src/H5Fint.c line 554 in H5F_is_hdf5(): unable to open file\n",
      "    major: Low-level I/O\n",
      "    minor: Unable to initialize object\n",
      "  #002: ../../../src/H5FD.c line 993 in H5FD_open(): open failed\n",
      "    major: Virtual File Layer\n",
      "    minor: Unable to initialize object\n",
      "  #003: ../../../src/H5FDsec2.c line 339 in H5FD_sec2_open(): unable to open file: name = 'sqwg-ex-000060000.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0\n",
      "    major: File accessibilty\n",
      "    minor: Unable to open file\n"
     ]
    },
    {
     "ename": "LoadError",
     "evalue": "Cannot access file sqwg-ex-000060000.h5",
     "output_type": "error",
     "traceback": [
      "Cannot access file sqwg-ex-000060000.h5",
      "",
      " in h5f_is_hdf5(::String) at /home/qxd/.julia/v0.5/HDF5/src/plain.jl:2132",
      " in h5open(::String, ::Bool, ::Bool, ::Bool, ::Bool, ::Bool, ::HDF5.HDF5Properties, ::HDF5.HDF5Properties) at /home/qxd/.julia/v0.5/HDF5/src/plain.jl:566",
      " in h5open(::String, ::String) at /home/qxd/.julia/v0.5/HDF5/src/plain.jl:598",
      " in h5read(::String, ::String) at /home/qxd/.julia/v0.5/HDF5/src/plain.jl:634"
     ]
    }
   ],
   "source": [
    "# Plot the Ex component in the cross sections.\n",
    "xzgrid = repmat(x,1,lenz); zxgrid = repmat(z',lenx,1)\n",
    "println(size(xzgrid)); println(size(zxgrid))\n",
    "# Read out the Ex, Ey and Ez data at time 10 (all real in the simulation).\n",
    "ex = h5read(join(datafilenames[3]), \"ex.r\") + im*h5read(join(datafilenames[3]), \"ex.i\")\n",
    "ex = permutedims(ex,[3,2,1])\n",
    "# println(ex[1]) # To verify if this is a real number.\n",
    "ey = h5read(join(datafilenames[4]), \"ey.r\") + im*h5read(join(datafilenames[4]), \"ey.i\")\n",
    "ey = permutedims(ey,[3,2,1])\n",
    "ez = h5read(join(datafilenames[5]), \"ez.r\") + im*h5read(join(datafilenames[5]), \"ez.i\")\n",
    "ez = permutedims(ez,[3,2,1])\n",
    "Int10 = abs(ex).^2+abs(ey).^2+abs(ez).^2;\n",
    "Int10z0 = Int10[:,:,ceil(Int64,lenz/2)+10]; # The xy-plane intensity at the 10th slice on the z-direction off the origin.\n",
    "maxInt10z0 = maximum(Int10z0[:]);\n",
    "Int10y0 = Int10[:,ceil(Int64,leny/2)+10,:]; # The xz-plane intensity at the 10th slice on the y-direction off the origin.\n",
    "\n",
    "# Read out the data at time 240.\n",
    "ex = h5read(join(datafilenames[18]), \"ex.r\") + im*h5read(join(datafilenames[18]), \"ex.i\")\n",
    "ex = permutedims(ex,[3,2,1])\n",
    "ey = h5read(join(datafilenames[19]), \"ey.r\") + im*h5read(join(datafilenames[19]), \"ey.i\")\n",
    "ey = permutedims(ey,[3,2,1])\n",
    "ez = h5read(join(datafilenames[20]), \"ez.r\") + im*h5read(join(datafilenames[20]), \"ez.i\")\n",
    "ez = permutedims(ez,[3,2,1])\n",
    "Int60 = abs(ex).^2+abs(ey).^2+abs(ez).^2;\n",
    "Int60z0 = Int60[:,:,ceil(Int64,lenz/2)+10]; # The intensity at the 10th slice on the z-direction off the origin.\n",
    "Int60y0 = Int60[:,ceil(Int64,leny/2)+10,:]; # The xz-plane intensity at the 10th slice on the y-direction off the origin.\n",
    "\n",
    "# Define the border of the waveguide on xy and xz cross-sections.\n",
    "wglen=0.3;\n",
    "xy_border_x=[-wglen/2.,wglen/2.,wglen/2.,-wglen/2.,-wglen/2.]\n",
    "xy_border_y=[wglen/2.,wglen/2.,-wglen/2.,-wglen/2.,wglen/2.]\n",
    "xz_border1_x=[-wglen/2.,-wglen/2.]\n",
    "xz_border1_z=[-1,1]\n",
    "xz_border2_x=[wglen/2.,wglen/2.]\n",
    "xz_border2_z=[-1,1]\n",
    "fig = figure(\"index of refraction plot\",figsize=(10,10))\n",
    "ax = fig[:add_subplot](2,2,1)\n",
    "pcolor(xygrid,yxgrid,Int10z0./maxInt10z0, cmap=\"RdBu\")\n",
    "#colorbar(;orientation=\"vertical\",fraction=0.04)\n",
    "plot(xy_border_x,xy_border_y,\"-k\")\n",
    "title(\"\\$I_{xy}(t=40)\\$ in the XY plane\")\n",
    "xlabel(L\"x/ \\mu m\")\n",
    "ylabel(L\"y/ \\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.3,0.3)\n",
    "ylim(-0.3,0.3)\n",
    "tight_layout()\n",
    "\n",
    "subplot(2,2,2)\n",
    "ax = fig[:add_subplot](2,2,2)\n",
    "pcolor(xygrid,yxgrid,Int60z0./maxInt10z0, cmap=\"RdBu\")\n",
    "colorbar(;orientation=\"vertical\",fraction=0.05)\n",
    "plot(xy_border_x,xy_border_y,\"-k\")\n",
    "title(\"\\$I_{xy}(t=240)\\$ in the XY plane\")\n",
    "xlabel(L\"x/ \\mu m\")\n",
    "ylabel(L\"y/ \\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.3,0.3)\n",
    "ylim(-0.3,0.3)\n",
    "tight_layout()\n",
    "\n",
    "subplot(2,2,3)\n",
    "ax = fig[:add_subplot](2,2,3)\n",
    "pcolor(xzgrid,zxgrid,Int10y0./maxInt10z0, cmap=\"RdBu\")\n",
    "#colorbar(;orientation=\"vertical\",fraction=0.05)\n",
    "plot(xz_border1_x,xz_border1_z,\"-k\")\n",
    "plot(xz_border2_x,xz_border2_z,\"-k\")\n",
    "title(\"\\$I_{xz}(t=40)\\$ in the XZ plane\")\n",
    "xlabel(L\"x/ \\mu m\")\n",
    "ylabel(L\"z/ \\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.6,0.6)\n",
    "ylim(-0.6,0.6)\n",
    "tight_layout()\n",
    "\n",
    "subplot(2,2,4)\n",
    "ax = fig[:add_subplot](2,2,4)\n",
    "pcolor(xzgrid,zxgrid,Int60y0./maxInt10z0, cmap=\"RdBu\")\n",
    "colorbar(;orientation=\"vertical\",fraction=0.05)\n",
    "plot(xz_border1_x,xz_border1_z,\"-k\")\n",
    "plot(xz_border2_x,xz_border2_z,\"-k\")\n",
    "title(\"\\$I_{xz}(t=240)\\$ in the XZ plane\")\n",
    "xlabel(L\"x/ \\mu m\")\n",
    "ylabel(L\"z/ \\mu m\")\n",
    "axis(\"image\")\n",
    "xlim(-0.6,0.6)\n",
    "ylim(-0.6,0.6)\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a ***slice*** view in a 3D fashion as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local field evolution\n",
    "\n",
    "This should be obtained by setting up a point-monitor at the dipole position and append time-dependent field components in some short periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we can see more easily that the fields are oscillating within the waveguide and are exponentially decaying outside the waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ex.i                     Dataset {1200/Inf}\n",
      "ex.r                     Dataset {1200/Inf}\n",
      "ey.i                     Dataset {1200/Inf}\n",
      "ey.r                     Dataset {1200/Inf}\n",
      "ez.i                     Dataset {1200/Inf}\n",
      "ez.r                     Dataset {1200/Inf}\n"
     ]
    }
   ],
   "source": [
    "run(`h5ls sqwg-Ert.h5`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f88ca6eb690>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1200,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6.676845942248249e-16"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using HDF5\n",
    "ex = h5read(\"sqwg-Ert.h5\", \"ex.r\") + im*h5read(\"sqwg-Ert.h5\", \"ex.i\")\n",
    "ey = h5read(\"sqwg-Ert.h5\", \"ey.r\") + im*h5read(\"sqwg-Ert.h5\", \"ey.i\")\n",
    "ez = h5read(\"sqwg-Ert.h5\", \"ez.r\") + im*h5read(\"sqwg-Ert.h5\", \"ez.i\")\n",
    "println(size(ex))\n",
    "lent=length(ex)\n",
    "ex = ex[end:-1:1]; ey = ey[end:-1:1]; ez = ez[end:-1:1];\n",
    "t=linspace(0,60.0,lent)\n",
    "figure(figsize=(12,3))\n",
    "subplot(1,3,1)\n",
    "ax=plot(t,real(ex))\n",
    "plot(t,imag(ex))\n",
    "title(\"\\$E_x(t)\\$ \")\n",
    "xlabel(L\"t\")\n",
    "ylabel(L\"$E_i$ (arbitrary units)\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)\n",
    "\n",
    "subplot(1,3,2)\n",
    "ax=plot(t,real(ey))\n",
    "plot(t,imag(ey))\n",
    "title(\"\\$E_y(t)\\$\")\n",
    "xlabel(L\"t\")\n",
    "#ylabel(L\"$E_y$ (arbitrary units)\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)\n",
    "\n",
    "subplot(1,3,3)\n",
    "ax=plot(t,real(ez))\n",
    "plot(t,imag(ez))\n",
    "title(\"\\$E_z(t)\\$\")\n",
    "xlabel(L\"t\")\n",
    "#ylabel(L\"$E_z$ (arbitrary units)\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)\n",
    "deltaT=240.0/0.895*895e-17/2.99792458/(lent-1) # $\\Delta T$ in SI units. In MEEP, time is in the unit of a/c where a is the unit of distance. See http://ab-initio.mit.edu/wiki/index.php/Meep_Introduction  or http://meepunits.wikia.com/wiki/Meep_unit_transformation_Wiki .\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we can plot out the detected signal in the frequency domain correspondingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f88c8fa8310>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.legend.Legend object at 0x7f88c8dd8490>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex_f=fft(ex); ey_f=fft(ey); ez_f=fft(ez);\n",
    "#ex_f=fftshift(ex_f); ey_f=fftshift(ey_f); ez_f=fftshift(ez_f); # Have to shift the frequency domain data for complex singal. See: http://www.exegetic.biz/blog/2015/10/monthofjulia-day-37-fourier-techniques/ \n",
    "freq_scale=linspace(0,1/deltaT,lent)/1e12;\n",
    "figure(figsize=(18,3))\n",
    "subplot(1,3,1)\n",
    "plot(freq_scale,real(ex_f))\n",
    "plot(freq_scale,imag(ex_f))\n",
    "xlabel(L\"f (THz)\")\n",
    "xlim([320,350])\n",
    "ylabel(L\"$E_i(f)$ (arbitrary units)\")\n",
    "title(\"\\$E_x(f)\\$\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)\n",
    "\n",
    "subplot(1,3,2)\n",
    "plot(freq_scale,real(ey_f))\n",
    "plot(freq_scale,imag(ey_f))\n",
    "xlabel(L\"f (THz)\")\n",
    "xlim([320,350])\n",
    "#xticks(linspace(800,960,5))\n",
    "title(\"\\$E_y(f)\\$\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)\n",
    "\n",
    "subplot(1,3,3)\n",
    "plot(freq_scale,real(ez_f))\n",
    "plot(freq_scale,imag(ez_f))\n",
    "xlabel(L\"f (THz)\")\n",
    "xlim([320,350])\n",
    "#xticks(linspace(800,960,5))\n",
    "title(\"\\$E_x(f)\\$\")\n",
    "legend([\"Real\", \"Imag\"],fontsize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can see that the peak is around $f_0=335THz$ which corresponding to a vacuum wavelength of $\\lambda_0=\\frac{c}{f_0}$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.949028597014926e-7"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda0=2.99792458e-4/335"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which correctly corresponds to our input light wavelength or $895$nm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result about can give the local Green's function elements at the dipole source position. To calculate the Purcell factor, the rest is to scale the peak to a correct unit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.5.1",
   "language": "julia",
   "name": "julia-0.5"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
